In this paper, we briefly review the development of ranking and selection (R&S) in the past
70 years, especially the theoretical achievements and practical applications in the past 20 …

Performance of machine learning algorithms depends critically on identifying a good set of
hyperparameters. While recent approaches use Bayesian optimization to adaptively select …

This paper is concerned with identifying the arm with the highest mean in a multi-armed
bandit problem using as few independent samples from the arms as possible. While the so …

Motivated by the task of hyperparameter optimization, we introduce the\em non-stochastic
best-arm identification problem. We identify an attractive algorithm for this setting that makes …

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in
many different contexts in statistics and machine learning. Whereas the achievable limit in …

Safe anytime-valid inference (SAVI) provides measures of statistical evidence and certainty—
e-processes for testing and confidence sequences for estimation—that remain valid at all …

Time-uniform, nonparametric, nonasymptotic confidence sequences Page 1 The Annals of
Statistics 2021, Vol. 49, No. 2, 1055–1080 https://doi.org/10.1214/20-AOS1991 © Institute of …

We give a complete characterization of the complexity of best-arm identification in one-
parameter bandit problems. We prove a new, tight lower bound on the sample complexity …

Top two algorithms arose as an adaptation of Thompson sampling to best arm identification
in multi-armed bandit models for parametric families of arms. They select the next arm to …

Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in
healthcare and the tech industry. They involve online learning algorithms that adaptively …